Netmetrics: Exploring Estimation Bias, Efficiency and Inference in Partial Political Networks

Posted: 2 Sep 2012

See all articles by Zining Yang

Zining Yang

Claremont Colleges - Claremont Graduate University

Patrick Neal

Claremont Colleges - Claremont Graduate University

Mark Abdollahian

Claremont Colleges - Claremont Graduate University

Date Written: August 31, 2012

Abstract

Advanced analytics are an integral inferential component for knowledge discovery and dissemination. We employ network metrics on relational variables of interest to control for econometric estimation bias, inefficiency and inferential problems from non-spherical, network induced disturbances in sampled, unknown political networks. Our analysis follows a two-step procedure to establish a conceptual baseline and then integrate real-world data and questions pertinent for statistical inference. First, artificial networks are generated using two well-known graph models, Erdos-Renyi and Barabasi-Albert. Simulated, synthetic data is created across a range of graph sizes with multiple node-level network attributes and estimation bias at different sample sizes. This gives a picture of bias, efficiency and consistency across network metrics, controlling for varied true-population sampling ratios. Second, we generate artificial networks across a broad range of graph model types compared to real networks from Correlates of War dyadic trade data. We then examine the consistency of each chosen metric across a wider range of possible network-creation processes. This information is visualized against one known and one potentially unknown statistic, R2 and node sample size. Our unique approach provides analytical and estimative insights for opaque and dynamic real world political networks with strategic, adaptive human behavior.

Keywords: Econometric Estimation, Inference, Bias, Efficiency, Partial Networks, Bayesian Networks

Suggested Citation

Yang, Zining and Neal, Patrick and Abdollahian, Mark, Netmetrics: Exploring Estimation Bias, Efficiency and Inference in Partial Political Networks (August 31, 2012). Available at SSRN: https://ssrn.com/abstract=2139782

Zining Yang (Contact Author)

Claremont Colleges - Claremont Graduate University ( email )

150 E. Tenth Street
Claremont, CA 91711
United States

Patrick Neal

Claremont Colleges - Claremont Graduate University ( email )

150 E. Tenth Street
Claremont, CA 91711
United States

Mark Abdollahian

Claremont Colleges - Claremont Graduate University ( email )

150 E. Tenth Street
Claremont, CA 91711
United States

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